MOVIEMOD: An implementable decision-support system for prerelease market evaluation of motion pictures

In spite of the high financial stakes involved in marketing new motion pict ures, marketing science models have not been applied to the prerelease mark et evaluation of motion pictures. The motion picture industry poses some un ique challenges. For example, the consumer adoption process for movies is v ery sensitive to word-of-mouth interactions, which are difficult to measure and predict before the movie has been released. In this article, we undert ake the challenge to develop and implement MOVIEMOD-a prerelease market eva luation model for the motion picture industry. MOVIEMOD is designed to gene rate box-office forecasts and to support marketing decisions for a new movi e after the movie has been produced (or when it is available in a rough cut ) but before it has been released. Unlike other forecasting models for moti on pictures, the calibration of MOVIEMOD does not require any actual sales data. Also, the data collection time for a product with a limited lifetime such as a movie should not take too long. For MOVIEMOD it takes only three hours in a "consumer clinic" to collect the data needed for the prediction of box-office sales and the evaluation of alternative marketing plans. The model is based on a behavioral representation of the consumer adoption process for movies as a macroflow process. The heart of MOVIEMOD is an inte ractive Markov chain model describing the macro-flow process. According to this model, at any point in time with respect to the movie under study, a c onsumer can be found in one of the following behavioral states: undecided, considerer, rejecter, positive spreader, negative spreader, and inactive. T he progression of consumers through the behavioral states depends on a set of movie-specific factors that are related to the marketing mix, as well as on a set of more general behavioral factors that characterize the movie-go ing behavior in the population of interest. This interactive Markov chain m odel allows us to account for word-of-mouth interactions among potential ad opters and several types of word-of-mouth spreaders in the population. Mark eting variables that influence the transitions among the states are movie t heme acceptability, promotion strategy, distribution strategy, and the movi e experience. The model is calibrated in a consumer clinic experiment. Resp ondents fill out a questionnaire with general items related to their movie- going and movie communication behavior, they are exposed to different sets of information stimuli, they are actually shown the movie, and finally, the y fill out postmovie evaluations, including word-of-mouth intentions. These measures are used to estimate the word-of-mouth parameters and other behav ioral factors, as well as the movie-specific parameters of the model. MOVIEMOD produces forecasts of the awareness, adoption intention, and cumul ative penetration for a new movie within the population of interest for a g iven base marketing plan. It also provides diagnostic information on the li kely impact of alternative marketing plans on the commercial performance of a new movie. We describe two applications of MOVIEMOD: One is a pilot stud y conducted without studio cooperation in the United States, and the other is a full-fledged implementation conducted with cooperation of the movie's distributor and exhibitor in the Netherlands. The implementations suggest t hat MOVIEMOD produces reasonably accurate forecasts of box-office performan ce. More importantly, the model offers the opportunity to simulate the effe cts of alternative marketing plans. In the Dutch application, the effects o f extra advertising, extra magazine articles, extra TV commercials, and hig her trailer intensity (compared to the base marketing plan of the distribut or) were analyzed. We demonstrate the value of these decision-support capab ilities of MOVIEMOD in assisting managers to identify a final plan that res ulted in an almost 50% increase in the Lest movie's revenue performance, co mpared to the marketing plan initially contemplated. Management implemented this recommended plan, which resulted in bur-office sales that were within 5% of the MOVIEMOD prediction. MOVIEMOD was also tested against several be nchmark models, and its prediction was better in all cases. An evaluation of MOVIEMOD jointly by the Dutch exhibitor and the distributo r showed that both parties were positive about and appreciated its performa nce as a decision-support tool. In particular, the distributor, who has mor e stakes in the domestic performance of its movies, showed a great interest in using MOYIEMOD for subsequent evaluations of new movies prior to their release. Based on such evaluations and thr initial validation results, MOVI EMOD can fruitfully (and inexpensively) be used to provide researchers and managers with a deeper understanding of the factors that drive audience res ponse to new motion pictures, and it can be instrumental in developing othe r decision-support systems that can improve the odds of commercial success of new experiential products.